Integrating Synthetic Data Generation Techniques for Enhanced Environmental Modeling

Tabular datasets form the backbone of environmental science research, supporting analyses in air quality monitoring, hydrology, soil characterization, agricultural productivity, climate studies, and water quality assessment. Despite their centrality, these datasets routinely suffer from scarcity, privacy constraints, measurement noise, and missing observations, which restrict the reliability of downstream modeling. In recent years, synthetic data generation has emerged as a promising strategy to address these limitations by producing privacy-preserving surrogate datasets that retain the key statistical properties of real measurements. This survey reviews major classes of generative models for tabular environmental data, including CTGAN, Gaussian Copula models, Autoregressive GANs, Variational Autoencoders, and newly introduced Diffusion-based approaches. Their capabilities are examined in relation to traditional receptor and source apportionment techniques such as Positive Matrix Factorization, Non-negative Matrix Factorization, Absolute Principal Component Analysis, Chemical Mass Balance, and PCA-based regression. Applications across atmospheric, hydrological, agricultural, meteorological, and water quality domains are synthesized to evaluate how synthetic data can support augmentation, reduce uncertainty, and improve model robustness. Cross-domain challenges-including multicollinearity, identifiability, small sample regimes, noise and missing data, and the lack of ground-truth for validation-are critically analyzed. Finally, the survey outlines a conceptual workflow for integrating synthetic data into environmental modeling pipelines and provides practical considerations for researchers aiming to enhance data reliability and methodological transparency.